Overview

Dataset statistics

Number of variables15
Number of observations7928
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory929.2 KiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

Time is highly correlated with 轉速 and 13 other fieldsHigh correlation
T1 is highly correlated with Time and 13 other fieldsHigh correlation
T2 is highly correlated with Time and 13 other fieldsHigh correlation
T3 is highly correlated with Time and 13 other fieldsHigh correlation
T4 is highly correlated with Time and 13 other fieldsHigh correlation
T5 is highly correlated with Time and 13 other fieldsHigh correlation
T6 is highly correlated with Time and 13 other fieldsHigh correlation
T7 is highly correlated with Time and 13 other fieldsHigh correlation
T8 is highly correlated with Time and 13 other fieldsHigh correlation
T9 is highly correlated with Time and 13 other fieldsHigh correlation
T10 is highly correlated with Time and 13 other fieldsHigh correlation
T11 is highly correlated with Time and 13 other fieldsHigh correlation
T12 is highly correlated with Time and 13 other fieldsHigh correlation
Z is highly correlated with Time and 13 other fieldsHigh correlation
轉速 is highly correlated with Time and 13 other fieldsHigh correlation
Time is uniformly distributed Uniform
Time has unique values Unique

Reproduction

Analysis started2022-11-11 03:25:42.401066
Analysis finished2022-11-11 03:25:53.100262
Duration10.7 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Time
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7928
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.2949996
Minimum0
Maximum660.5866667
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.128168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.0325
Q1165.1491667
median330.295
Q3495.4408333
95-th percentile627.5575
Maximum660.5866667
Range660.5866667
Interquartile range (IQR)330.2916667

Descriptive statistics

Standard deviation190.7300674
Coefficient of variation (CV)0.5774536933
Kurtosis-1.199999982
Mean330.2949996
Median Absolute Deviation (MAD)165.1666667
Skewness-1.32251014 × 10-8
Sum2618578.757
Variance36377.95861
MonotonicityStrictly increasing
2022-11-11T11:25:53.252748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
441.25333331
 
< 0.1%
441.08666671
 
< 0.1%
441.00333331
 
< 0.1%
440.921
 
< 0.1%
440.83666671
 
< 0.1%
440.75333331
 
< 0.1%
440.671
 
< 0.1%
440.58666671
 
< 0.1%
440.50333331
 
< 0.1%
Other values (7918)7918
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.086666666671
< 0.1%
0.171
< 0.1%
0.25333333331
< 0.1%
0.33666666671
< 0.1%
0.421
< 0.1%
0.50333333331
< 0.1%
0.58666666671
< 0.1%
0.671
< 0.1%
0.75333333331
< 0.1%
ValueCountFrequency (%)
660.58666671
< 0.1%
660.50333331
< 0.1%
660.421
< 0.1%
660.33666671
< 0.1%
660.25333331
< 0.1%
660.171
< 0.1%
660.08666671
< 0.1%
660.00333331
< 0.1%
659.921
< 0.1%
659.83666671
< 0.1%

轉速
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
24000
4311 
0
3329 
12000
 
141
3000
 
75
20000
 
72

Length

Max length5
Median length5
Mean length3.31092331
Min length1

Characters and Unicode

Total characters26249
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000
2nd row3000
3rd row3000
4th row3000
5th row3000

Common Values

ValueCountFrequency (%)
240004311
54.4%
03329
42.0%
12000141
 
1.8%
300075
 
0.9%
2000072
 
0.9%

Length

2022-11-11T11:25:53.310334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:53.361668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
240004311
54.4%
03329
42.0%
12000141
 
1.8%
300075
 
0.9%
2000072
 
0.9%

Most occurring characters

ValueCountFrequency (%)
017198
65.5%
24524
 
17.2%
44311
 
16.4%
1141
 
0.5%
375
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26249
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017198
65.5%
24524
 
17.2%
44311
 
16.4%
1141
 
0.5%
375
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common26249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017198
65.5%
24524
 
17.2%
44311
 
16.4%
1141
 
0.5%
375
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017198
65.5%
24524
 
17.2%
44311
 
16.4%
1141
 
0.5%
375
 
0.3%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.58100404
Minimum24
Maximum30.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.411995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile25.1
Q126
median28
Q329.2
95-th percentile29.9
Maximum30.1
Range6.1
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.780024713
Coefficient of variation (CV)0.06453806796
Kurtosis-1.541564473
Mean27.58100404
Median Absolute Deviation (MAD)1.8
Skewness-0.09466657254
Sum218662.2
Variance3.168487979
MonotonicityNot monotonic
2022-11-11T11:25:53.468544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261268
16.0%
25.2927
 
11.7%
29.8736
 
9.3%
29.1614
 
7.7%
25.1447
 
5.6%
29.9375
 
4.7%
28326
 
4.1%
26.6266
 
3.4%
29.2263
 
3.3%
27.9247
 
3.1%
Other values (109)2459
31.0%
ValueCountFrequency (%)
2420
0.3%
24.051
 
< 0.1%
24.13
 
< 0.1%
24.151
 
< 0.1%
24.23
 
< 0.1%
24.251
 
< 0.1%
24.36
 
0.1%
24.351
 
< 0.1%
24.47
 
0.1%
24.451
 
< 0.1%
ValueCountFrequency (%)
30.1135
 
1.7%
30.052
 
< 0.1%
30121
 
1.5%
29.952
 
< 0.1%
29.9375
4.7%
29.852
 
< 0.1%
29.8736
9.3%
29.752
 
< 0.1%
29.711
 
0.1%
29.652
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.2681004
Minimum23.4
Maximum26.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.523359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.4
5-th percentile23.6
Q125
median25.3
Q325.9
95-th percentile26.4
Maximum26.5
Range3.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7822928871
Coefficient of variation (CV)0.03095970313
Kurtosis-0.2185111482
Mean25.2681004
Median Absolute Deviation (MAD)0.6
Skewness-0.7110992601
Sum200325.5
Variance0.6119821612
MonotonicityNot monotonic
2022-11-11T11:25:53.571261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
261247
15.7%
25.2951
 
12.0%
25.1733
 
9.2%
25.9712
 
9.0%
25.8357
 
4.5%
26.5346
 
4.4%
25.5327
 
4.1%
25287
 
3.6%
25.4258
 
3.3%
25.7210
 
2.6%
Other values (22)2500
31.5%
ValueCountFrequency (%)
23.4175
2.2%
23.585
1.1%
23.6152
1.9%
23.7134
1.7%
23.8150
1.9%
23.948
 
0.6%
24116
1.5%
24.182
1.0%
24.2135
1.7%
24.3135
1.7%
ValueCountFrequency (%)
26.5346
 
4.4%
26.476
 
1.0%
26.312
 
0.2%
26.213
 
0.2%
26.1148
 
1.9%
261247
15.7%
25.9712
9.0%
25.8357
 
4.5%
25.7210
 
2.6%
25.6146
 
1.8%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.13615035
Minimum23.2
Maximum26.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.622804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile23.5
Q124.7
median25.2
Q325.8
95-th percentile26.4
Maximum26.5
Range3.3
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.8231879273
Coefficient of variation (CV)0.03274916468
Kurtosis-0.4031965551
Mean25.13615035
Median Absolute Deviation (MAD)0.5
Skewness-0.5688470828
Sum199279.4
Variance0.6776383637
MonotonicityNot monotonic
2022-11-11T11:25:53.671913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
25.21254
15.8%
261080
 
13.6%
25.1581
 
7.3%
26.5389
 
4.9%
25.7388
 
4.9%
25.9379
 
4.8%
25327
 
4.1%
25.6305
 
3.8%
25.4227
 
2.9%
24.8219
 
2.8%
Other values (24)2779
35.1%
ValueCountFrequency (%)
23.289
1.1%
23.3100
1.3%
23.479
1.0%
23.5158
2.0%
23.6141
1.8%
23.7175
2.2%
23.8122
1.5%
23.9143
1.8%
24155
2.0%
24.1190
2.4%
ValueCountFrequency (%)
26.5389
 
4.9%
26.434
 
0.4%
26.39
 
0.1%
26.28
 
0.1%
26.19
 
0.1%
261080
13.6%
25.9379
 
4.8%
25.8198
 
2.5%
25.7388
 
4.9%
25.6305
 
3.8%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.97931382
Minimum24
Maximum27.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.724739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24.7
Q125.3
median26.1
Q326.6
95-th percentile27.1
Maximum27.3
Range3.3
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.7585671882
Coefficient of variation (CV)0.02919889237
Kurtosis-0.6325338571
Mean25.97931382
Median Absolute Deviation (MAD)0.7
Skewness-0.2587791792
Sum205964
Variance0.575424179
MonotonicityNot monotonic
2022-11-11T11:25:53.774633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
25.31310
16.5%
26.1905
 
11.4%
26.2424
 
5.3%
25.4377
 
4.8%
26.3344
 
4.3%
26.4330
 
4.2%
26.5314
 
4.0%
26.6312
 
3.9%
27.2304
 
3.8%
26.9288
 
3.6%
Other values (24)3020
38.1%
ValueCountFrequency (%)
2472
0.9%
24.141
 
0.5%
24.232
 
0.4%
24.339
 
0.5%
24.447
0.6%
24.560
0.8%
24.663
0.8%
24.774
0.9%
24.892
1.2%
24.9107
1.3%
ValueCountFrequency (%)
27.368
 
0.9%
27.2304
3.8%
27.1272
3.4%
27279
3.5%
26.9288
3.6%
26.8269
3.4%
26.7278
3.5%
26.6312
3.9%
26.5314
4.0%
26.4330
4.2%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.87819122
Minimum24.2
Maximum26.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.824258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.2
5-th percentile24.4
Q125.5
median26.1
Q326.4
95-th percentile26.8
Maximum26.8
Range2.6
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6973828114
Coefficient of variation (CV)0.02694866907
Kurtosis-0.2685106142
Mean25.87819122
Median Absolute Deviation (MAD)0.5
Skewness-0.7672127247
Sum205162.3
Variance0.4863427857
MonotonicityNot monotonic
2022-11-11T11:25:53.873096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
26.41382
17.4%
26.3984
12.4%
25.6872
 
11.0%
25.5710
 
9.0%
26.8554
 
7.0%
26.6272
 
3.4%
26.7265
 
3.3%
26.5218
 
2.7%
24.2214
 
2.7%
26.2209
 
2.6%
Other values (17)2248
28.4%
ValueCountFrequency (%)
24.2214
2.7%
24.3131
1.7%
24.4102
1.3%
24.5128
1.6%
24.6110
1.4%
24.791
1.1%
24.8120
1.5%
24.9106
1.3%
2596
1.2%
25.1130
1.6%
ValueCountFrequency (%)
26.8554
7.0%
26.7265
 
3.3%
26.6272
 
3.4%
26.5218
 
2.7%
26.41382
17.4%
26.3984
12.4%
26.2209
 
2.6%
26.1173
 
2.2%
26193
 
2.4%
25.9153
 
1.9%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.58714682
Minimum24.3
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:53.920934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.5
Q125
median25.8
Q326.1
95-th percentile26.4
Maximum26.4
Range2.1
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.6182457941
Coefficient of variation (CV)0.0241623577
Kurtosis-1.029778139
Mean25.58714682
Median Absolute Deviation (MAD)0.5
Skewness-0.4508386239
Sum202854.9
Variance0.3822278619
MonotonicityIncreasing
2022-11-11T11:25:53.965241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
25.9972
 
12.3%
26.4656
 
8.3%
26.2616
 
7.8%
25606
 
7.6%
26.3586
 
7.4%
26433
 
5.5%
26.1418
 
5.3%
25.7356
 
4.5%
24.5334
 
4.2%
25.1327
 
4.1%
Other values (12)2624
33.1%
ValueCountFrequency (%)
24.3114
 
1.4%
24.4229
 
2.9%
24.5334
4.2%
24.6103
 
1.3%
24.7250
3.2%
24.8239
 
3.0%
24.9123
 
1.6%
25606
7.6%
25.1327
4.1%
25.2291
3.7%
ValueCountFrequency (%)
26.4656
8.3%
26.3586
7.4%
26.2616
7.8%
26.1418
5.3%
26433
5.5%
25.9972
12.3%
25.8302
 
3.8%
25.7356
 
4.5%
25.6319
 
4.0%
25.5226
 
2.9%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.0355449
Minimum23.2
Maximum26.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.013049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile23.4
Q124.6
median25.2
Q325.6
95-th percentile26.2
Maximum26.5
Range3.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8470388947
Coefficient of variation (CV)0.03383345152
Kurtosis-0.4480172335
Mean25.0355449
Median Absolute Deviation (MAD)0.5
Skewness-0.5543854295
Sum198481.8
Variance0.7174748891
MonotonicityNot monotonic
2022-11-11T11:25:54.059891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
25.2878
 
11.1%
26873
 
11.0%
25.1645
 
8.1%
25.9435
 
5.5%
25.5406
 
5.1%
25389
 
4.9%
25.4349
 
4.4%
24.9304
 
3.8%
24.8269
 
3.4%
25.6250
 
3.2%
Other values (24)3130
39.5%
ValueCountFrequency (%)
23.2238
3.0%
23.3156
2.0%
23.4136
1.7%
23.5155
2.0%
23.6107
1.3%
23.7141
1.8%
23.8129
1.6%
23.9179
2.3%
24214
2.7%
24.174
 
0.9%
ValueCountFrequency (%)
26.5115
 
1.5%
26.4220
 
2.8%
26.354
 
0.7%
26.223
 
0.3%
26.112
 
0.2%
26873
11.0%
25.9435
5.5%
25.8141
 
1.8%
25.754
 
0.7%
25.6250
 
3.2%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.30440212
Minimum23.7
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.109723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.7
5-th percentile23.9
Q125
median25.4
Q325.9
95-th percentile26.4
Maximum26.4
Range2.7
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7183071964
Coefficient of variation (CV)0.02838664961
Kurtosis-0.574945587
Mean25.30440212
Median Absolute Deviation (MAD)0.5
Skewness-0.5786342234
Sum200613.3
Variance0.5159652285
MonotonicityNot monotonic
2022-11-11T11:25:54.222344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25.91550
19.6%
25.11193
15.0%
25.8545
 
6.9%
25447
 
5.6%
26.4425
 
5.4%
26.1321
 
4.0%
24283
 
3.6%
26253
 
3.2%
24.9245
 
3.1%
25.6241
 
3.0%
Other values (18)2425
30.6%
ValueCountFrequency (%)
23.7164
2.1%
23.8142
1.8%
23.9164
2.1%
24283
3.6%
24.181
 
1.0%
24.2119
1.5%
24.3169
2.1%
24.4156
2.0%
24.5139
1.8%
24.6114
1.4%
ValueCountFrequency (%)
26.4425
 
5.4%
26.323
 
0.3%
26.287
 
1.1%
26.1321
 
4.0%
26253
 
3.2%
25.91550
19.6%
25.8545
 
6.9%
25.7156
 
2.0%
25.6241
 
3.0%
25.5154
 
1.9%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct93
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.96012866
Minimum27
Maximum31.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.276163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28.8
Q129.1
median30
Q330.9
95-th percentile31.2
Maximum31.6
Range4.6
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation0.9365092288
Coefficient of variation (CV)0.03125851826
Kurtosis-0.8092759977
Mean29.96012866
Median Absolute Deviation (MAD)0.9
Skewness-0.2457424715
Sum237523.9
Variance0.8770495356
MonotonicityNot monotonic
2022-11-11T11:25:54.332899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29550
 
6.9%
29.1522
 
6.6%
29.2520
 
6.6%
28.9509
 
6.4%
31.1459
 
5.8%
31412
 
5.2%
30.9412
 
5.2%
31.2314
 
4.0%
30.8287
 
3.6%
30.6269
 
3.4%
Other values (83)3674
46.3%
ValueCountFrequency (%)
271
 
< 0.1%
27.051
 
< 0.1%
27.138
0.5%
27.151
 
< 0.1%
27.221
0.3%
27.251
 
< 0.1%
27.37
 
0.1%
27.351
 
< 0.1%
27.412
 
0.2%
27.451
 
< 0.1%
ValueCountFrequency (%)
31.623
 
0.3%
31.552
 
< 0.1%
31.515
 
0.2%
31.452
 
< 0.1%
31.466
 
0.8%
31.3518
 
0.2%
31.3174
2.2%
31.2530
 
0.4%
31.2314
4.0%
31.1532
 
0.4%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.78528002
Minimum26.2
Maximum32.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.389905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum26.2
5-th percentile28.4
Q130.1
median30.9
Q331.8
95-th percentile32.1
Maximum32.3
Range6.1
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.191891161
Coefficient of variation (CV)0.03871626829
Kurtosis2.050646743
Mean30.78528002
Median Absolute Deviation (MAD)0.8
Skewness-1.238754792
Sum244065.7
Variance1.420604539
MonotonicityNot monotonic
2022-11-11T11:25:54.445990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.1903
 
11.4%
30739
 
9.3%
30.1554
 
7.0%
32414
 
5.2%
30.3404
 
5.1%
30.2399
 
5.0%
31.8361
 
4.6%
31.7263
 
3.3%
31.4263
 
3.3%
30.7245
 
3.1%
Other values (52)3383
42.7%
ValueCountFrequency (%)
26.225
0.3%
26.329
0.4%
26.412
0.2%
26.511
 
0.1%
26.617
0.2%
26.76
 
0.1%
26.87
 
0.1%
26.910
 
0.1%
2719
0.2%
27.117
0.2%
ValueCountFrequency (%)
32.378
 
1.0%
32.2180
 
2.3%
32.1903
11.4%
32414
5.2%
31.9202
 
2.5%
31.8361
 
4.6%
31.7263
 
3.3%
31.6203
 
2.6%
31.5244
 
3.1%
31.4263
 
3.3%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.99732593
Minimum24.1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.500805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.1
5-th percentile25
Q125.7
median26
Q326.4
95-th percentile26.7
Maximum27
Range2.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5515650074
Coefficient of variation (CV)0.02121622081
Kurtosis1.046707596
Mean25.99732593
Median Absolute Deviation (MAD)0.4
Skewness-0.8711937491
Sum206106.8
Variance0.3042239573
MonotonicityNot monotonic
2022-11-11T11:25:54.547134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25.7884
11.2%
25.6860
10.8%
26.6739
 
9.3%
26.4708
 
8.9%
25.8556
 
7.0%
26.3502
 
6.3%
26.5500
 
6.3%
26.7431
 
5.4%
26.1427
 
5.4%
26.2401
 
5.1%
Other values (20)1920
24.2%
ValueCountFrequency (%)
24.173
0.9%
24.228
 
0.4%
24.320
 
0.3%
24.434
0.4%
24.554
0.7%
24.661
0.8%
24.748
0.6%
24.822
 
0.3%
24.944
0.6%
2520
 
0.3%
ValueCountFrequency (%)
2737
 
0.5%
26.960
 
0.8%
26.8139
 
1.8%
26.7431
5.4%
26.6739
9.3%
26.5500
6.3%
26.4708
8.9%
26.3502
6.3%
26.2401
5.1%
26.1427
5.4%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.01868063
Minimum24
Maximum27.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.598150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile25.1
Q125.6
median26.1
Q326.5
95-th percentile26.8
Maximum27.1
Range3.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6010695861
Coefficient of variation (CV)0.02310146294
Kurtosis0.5420020587
Mean26.01868063
Median Absolute Deviation (MAD)0.5
Skewness-0.7290751701
Sum206276.1
Variance0.3612846473
MonotonicityNot monotonic
2022-11-11T11:25:54.648564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
25.5772
 
9.7%
26.7765
 
9.6%
26.6705
 
8.9%
26.5599
 
7.6%
25.6559
 
7.1%
26.2549
 
6.9%
25.7537
 
6.8%
25.4526
 
6.6%
25.8420
 
5.3%
26.4348
 
4.4%
Other values (22)2148
27.1%
ValueCountFrequency (%)
2422
 
0.3%
24.179
1.0%
24.227
 
0.3%
24.343
0.5%
24.432
0.4%
24.544
0.6%
24.627
 
0.3%
24.733
0.4%
24.826
 
0.3%
24.920
 
0.3%
ValueCountFrequency (%)
27.16
 
0.1%
2786
 
1.1%
26.9112
 
1.4%
26.8303
 
3.8%
26.7765
9.6%
26.6705
8.9%
26.5599
7.6%
26.4348
4.4%
26.3321
4.0%
26.2549
6.9%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct148
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.35991435
Minimum0
Maximum81.047
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size62.1 KiB
2022-11-11T11:25:54.706369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.094
Q125.594
median73.125
Q379.219
95-th percentile79.219
Maximum81.047
Range81.047
Interquartile range (IQR)53.625

Descriptive statistics

Standard deviation28.59272202
Coefficient of variation (CV)0.5358464751
Kurtosis-1.407179035
Mean53.35991435
Median Absolute Deviation (MAD)7.313
Skewness-0.4900573779
Sum423037.401
Variance817.5437525
MonotonicityNot monotonic
2022-11-11T11:25:54.765171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.2192548
32.1%
78519
 
6.5%
79.8285242
 
3.1%
78.6095234
 
3.0%
8.531173
 
2.2%
6.094155
 
2.0%
4.875149
 
1.9%
7.313136
 
1.7%
80.438130
 
1.6%
3.656117
 
1.5%
Other values (138)3525
44.5%
ValueCountFrequency (%)
09
 
0.1%
0.60951
 
< 0.1%
1.2192
 
< 0.1%
1.82851
 
< 0.1%
2.43814
 
0.2%
3.0475
 
0.1%
3.656117
1.5%
4.265533
 
0.4%
4.875149
1.9%
5.484515
 
0.2%
ValueCountFrequency (%)
81.0472
 
< 0.1%
80.438130
 
1.6%
79.8285242
 
3.1%
79.2192548
32.1%
78.6095234
 
3.0%
78519
 
6.5%
77.390530
 
0.4%
76.78190
 
1.1%
76.1727
 
0.1%
75.56362
 
0.8%

Interactions

2022-11-11T11:25:52.221200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:42.942168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.628131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.371070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.059810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.813167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.501772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.229980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.887024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.645718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.383847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.037199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.780994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.468610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.272029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:42.993997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.676472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.420103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.108785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.863020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.548674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.276822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.936341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.694017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.430767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.085634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.830846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.518272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.322575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.043423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.724315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.470057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.158041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.912911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.595874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.323899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.985339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.742328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.476710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.133472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.880306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.568073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.373015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.093255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.773210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.518860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.207347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.963165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.642444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.371680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.107887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.791109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.523237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.183365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.930354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.617350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.423844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.143088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.822099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.569086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.257010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.012997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.690367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.419518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.157883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.840166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.570525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.232146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.981195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.667170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.474673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.192564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.871878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.619270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.307173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.063316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.810410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.467357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.207715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.889200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.617427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.281978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.031329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.717130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.522777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.239091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.918771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.666490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.353018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.110158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.855469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.512206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.253560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.935045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.663148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.327562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.077708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.763806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.570948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.284936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.964700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.713381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.399273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.156790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.899727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.556064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.299406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.980504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.707152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.374832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.124452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.810589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.621348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.334974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.013482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.763628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.513093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.206563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.948019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.603291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.349238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.029424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.754958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.423795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.174251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.860470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.671180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.383566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.061395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.812463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.562148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.254451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.993811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.650184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.398073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.076266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.799864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.471633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.222077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.974478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.719019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.429120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.107742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.859315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.607939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.301864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.037736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.694599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.444916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.122111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.844676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.517479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.269141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.021243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.769729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.478043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.221090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.908826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.657219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.350529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.085166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.741588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.493924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.170997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.892519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.566307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.317783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.071075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.819981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.527413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.269974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.958658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.707019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.400314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.133042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.789427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.543763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.219832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.939361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.680921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.367615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.120550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.869781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:43.576252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:44.320241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.008981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:45.758901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:46.449985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.180948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:47.837192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:48.593836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.267720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:49.987422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:50.730198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:51.417447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:52.169375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:25:54.822976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:25:54.898022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:25:54.975826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:25:55.053564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:25:55.197347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:25:52.956597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:25:53.063414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Time轉速T1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000300024.024.024.124.124.224.324.024.027.0026.224.124.00.0000
10.086667300024.024.024.124.124.224.324.024.027.0526.224.124.00.0000
20.170000300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
30.253333300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
40.336667300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
50.420000300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
60.503333300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
70.586667300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
80.670000300024.024.024.124.124.224.324.024.027.1026.224.124.00.0000
90.753333300024.024.024.124.024.224.324.024.027.1026.224.124.00.6095

Last rows

Time轉速T1T2T3T4T5T6T7T8T9T10T11T12Z
7918659.836667026.626.526.526.826.826.426.526.429.430.626.226.129.2500
7919659.920000026.626.526.526.826.826.426.526.429.430.626.226.129.2500
7920660.003333026.626.526.526.826.826.426.526.429.430.626.226.129.2500
7921660.086667026.626.526.526.826.826.426.526.429.430.626.226.129.2500
7922660.170000026.626.526.526.826.826.426.526.429.430.626.226.029.8595
7923660.253333026.626.526.526.826.826.426.526.429.430.626.226.129.8595
7924660.336667026.626.526.526.826.826.426.526.429.430.626.226.029.8595
7925660.420000026.626.526.526.826.826.426.526.429.430.626.226.029.8595
7926660.503333026.626.526.526.826.826.426.526.429.430.626.226.030.4690
7927660.586667026.626.526.526.826.826.426.526.429.430.626.226.029.8595